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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma
Ying-mei Zheng, Junyi Che, Ming-gang Yuan, et al.
Academic Radiology (2022) Vol. 30, Iss. 8, pp. 1591-1599
Closed Access | Times Cited: 22

Showing 22 citing articles:

Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours
Yangchun Du, Yanju Xiao, Wenwen Guo, et al.
BioMedical Engineering OnLine (2024) Vol. 23, Iss. 1
Open Access | Times Cited: 7
Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study
Yangchun Du, Wenwen Guo, Yanju Xiao, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 6
Deep learning radiomics model based on contrast-enhanced MRI for distinguishing between tuberculous spondylitis and pyogenic spondylitis
Xiaonan Yang, Na Tian, Yuzhu Zhang, et al.
European Spine Journal (2025)
Closed Access
A clinicoradiological model based on clinical and CT features for preoperative prediction of histological classification in patients with epithelial ovarian cancers: a two-center study
Jiao Jiao Li, Wenjiang Wang, Bin Zhang, et al.
Abdominal Radiology (2025)
Closed Access
CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study
Yongjie Zhou, Yuan Zhan, Jinhong Zhao, et al.
Insights into Imaging (2025) Vol. 16, Iss. 1
Open Access
Advances in the Application of Radiomics and Deep Learning for the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma
凯 谢
Advances in Clinical Medicine (2025) Vol. 15, Iss. 04, pp. 1680-1687
Closed Access
Machine learning-based multiparametric CT radiomics for predicting microvascular invasion before nephrectomy in clear cell renal cell carcinoma
Jinbin Xu, Shuntian Gao, Qin Zhu, et al.
Abdominal Radiology (2025)
Closed Access
CT-based deep learning radiomics biomarker for programmed cell death ligand 1 expression in non-small cell lung cancer
Ting Xu, Xiaowen Liu, Yaxi Chen, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 3
Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer
Linyong Wu, Songhua Li, Chaojun Wu, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 2
A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas
Tan Mai Nguyen, Chloé Bertolus, P Giraud, et al.
Cancers (2023) Vol. 15, Iss. 22, pp. 5369-5369
Open Access | Times Cited: 5
Machine Learning-Based MRI Radiogenomics for Evaluation of Response to Induction Chemotherapy in Head and Neck Squamous Cell Carcinoma
Zheng Li, Ru Wang, Lingwa Wang, et al.
Academic Radiology (2023) Vol. 31, Iss. 6, pp. 2464-2475
Closed Access | Times Cited: 5
Dermoscopy-based Radiomics Help Distinguish Basal Cell Carcinoma and Actinic Keratosis: A Large-scale Real-world Study Based on a 207-combination Machine Learning Computational Framework
Hewen Guan, Qihang Yuan, Kejia Lv, et al.
Journal of Cancer (2024) Vol. 15, Iss. 11, pp. 3350-3361
Open Access | Times Cited: 1
Computer Vision—Radiomics & Pathognomics
Alexandra T. Bourdillon
Otolaryngologic Clinics of North America (2024) Vol. 57, Iss. 5, pp. 719-751
Closed Access | Times Cited: 1
Prediction of Histological Grade of Oral Squamous Cell Carcinoma Using Machine Learning Models Applied to 18F-FDG-PET Radiomics
Yutaka Nikkuni, Hideyoshi Nishiyama, Takafumi Hayashi
Biomedicines (2024) Vol. 12, Iss. 7, pp. 1411-1411
Open Access | Times Cited: 1
A CT-based Deep Learning Radiomics Nomogram for the Prediction of EGFR Mutation Status in Head and Neck Squamous Cell Carcinoma
Ying-mei Zheng, Jing Pang, Zong-jing Liu, et al.
Academic Radiology (2023) Vol. 31, Iss. 2, pp. 628-638
Closed Access | Times Cited: 2
An MRI-Based Deep Learning Radiomics Nomogram Analysis for Prediction of Postoperative Lymph Node Metastasis in Patients with Locally Advanced Cervical Cancer Receiving Neoadjuvant Therapy
Rongrong Wu, Xingyun Xie, Yimin Zhou
Research Square (Research Square) (2024)
Open Access
Multitask Learning for Concurrent Grading Diagnosis and Semi-Supervised Segmentation of Honeycomb Lung in CT Images
Yunyun Dong, Bingqian Yang, Xiufang Feng
Electronics (2024) Vol. 13, Iss. 11, pp. 2115-2115
Open Access
Deep Transfer Learning-based 3D super-resolution Radiomics modeling of CT to discriminate Brucella Spondylitis from Tuberculous Spondylitis: A multicenter, retrospective study
Kaifeng Wang, Lixia Qi, Tingxin Zhang, et al.
Research Square (Research Square) (2024)
Open Access
Integrating lipid metabolite analysis with MRI-based transformer and radiomics for early and late stage prediction of oral squamous cell carcinoma
Wen Li, Yang Li, Shiyu Gao, et al.
BMC Cancer (2024) Vol. 24, Iss. 1
Open Access
Development and validation of a CT-based deep learning radiomics signature to predict lymph node metastasis in oropharyngeal squamous cell carcinoma: a multicenter study
Tianzi Jiang, Hexiang Wang, Jie Li, et al.
Dentomaxillofacial Radiology (2024)
Closed Access
Predicting craniofacial fibrous dysplasia growth status: An exploratory study of a hybrid radiomics and deep learning model based on computed tomography images
G. Li, Hao Liu, Zhengke Pan, et al.
Oral Surgery Oral Medicine Oral Pathology and Oral Radiology (2024)
Closed Access
A CT-Based Clinical-Radiomics Nomogram for Predicting the Overall Survival to TACE Combined with Camrelizumab and Apatinib in Patients with Advanced Hepatocellular Carcinoma
Guoshan Ding, Kailang Li
Academic Radiology (2024)
Closed Access
Radiomics Model Based on Contrast-enhanced CT Intratumoral and Peritumoral Features for Predicting Lymphovascular Invasion in Hypopharyngeal Squamous Cell Carcinoma
Chenyang Xu, Yuanrong Ju, Zhiwei Liu, et al.
Academic Radiology (2024)
Closed Access
An MRI-based radiomics nomogram for preoperative prediction of Ki-67 index in nasopharyngeal carcinoma: a two-center study
Yao Wang, Jing Zhang, Qiyuan Li, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access
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